Abstract: The success of machine learning (ML) in chemistry and biology is contingent on experimental efforts that generate relevant datasets and that validate model predictions. Critically, such experimentation often necessitates significant time and resource investments. Computational methods to inform experimental modeling could help alleviate this burden and bridge the gap between computational predictions and experimental validation. In this talk, I will discuss how ML algorithms that quantify prediction uncertainties could meet this critical need. Using molecular property prediction and drug discovery as a motivating use case, I will present a new method -- evidential deep learning -- for uncertainty quantification in neural networks and demonstrate its potential to (1) achieve calibrated estimates of model uncertainty, (2) improve sample efficiency via uncertainty-guided active learning, and (3) inform experimental validation via targeted virtual screening. I will close by highlighting how prediction uncertainty can accelerate and guide key steps in experimental lifecycles, opening the door for sustained feedback between computation and experimentation in the chemical and biological sciences.
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